Video Experimental Relacionado
Updated: Jan 28, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
Published on: January 18, 2020
ADAM-Net: Adaptación no supervisada guiada por anatomía y atención para la segmentación conjunta de glándulas de
Junbin Fang1,2,3,4, Xuan He1,2,3,4, You Jiang1,2,3,4
1Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
ADAM-Net, un novedoso marco de aprendizaje profundo, aborda eficazmente el cambio de dominio en imágenes multicéntricas para la evaluación automatizada de la disfunción de las glándulas de Meibomio (DGM). Logra una alta precisión en la clasificación de la DGM al realizar conjuntamente la segmentación y clasificación de las glándulas.
Conclusiones:
- ADAM-Net proporciona una solución eficaz y robusta para la evaluación automatizada de la DGM en escenarios multicéntricos.
- El marco exhibe una fuerte interpretabilidad y aborda los desafíos del cambio de dominio.
- El modelado conjunto de la segmentación y la clasificación mejora el rendimiento y la robustez.
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